Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs
Wang, Yongcui4,5; Chen, Shilong4; Chen, Luonan1; Wang, Yong2,3
刊名PLOS COMPUTATIONAL BIOLOGY
2019-12-01
卷号15期号:12页码:20
ISSN号1553-734X
DOI10.1371/journal.pcbi.1007540
英文摘要Long noncoding RNA (lncRNA) transcripts have emerging impacts in cancer studies, which suggests their potential as novel therapeutic agents. However, the molecular mechanism behind their treatment effects is still unclear. Here, we designed a computational model to Associate LncRNAs with Anti-Cancer Drugs (ALACD) based on a bilevel optimization model, which optimized the gene signature overlap in the upper level and imputed the missing lncRNA-gene association in the lower level. ALACD predicts genes coexpressed with lncRNAs mean while matching drug's gene signatures. This model allows us to borrow the target gene information of small molecules to understand the mechanisms of action of lncRNAs and their roles in cancer. The ALACD model was systematically applied to the 10 cancer types in The Cancer Genome Atlas (TCGA) that had matched lncRNA and mRNA expression data. Cancer type-specific lncRNAs and associated drugs were identified. These lncRNAs show significantly different expression levels in cancer patients. Follow-up functional and molecular pathway analysis suggest the gene signatures bridging drugs and lncRNAs are closely related to cancer development. Importantly, patient survival information and evidence from the literature suggest that the lncRNAs and drug-lncRNA associations identified by the ALACD model can provide an alternative choice for cancer targeting treatment and potential cancer pognostic biomarkers. The ALACD model is freely available at .
资助项目National Natural Science Foundation of China[11671396] ; National Natural Science Foundation of China[31270270] ; National Natural Science Foundation of China[61671444] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[11871463] ; Qinghai Sciences and Technology Department for Basic Research Program[2017-ZJ-Y14] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB13050100] ; National Key Research and Development Program of China[2017YFC0908400]
WOS研究方向Biochemistry & Molecular Biology ; Mathematical & Computational Biology
语种英语
出版者PUBLIC LIBRARY SCIENCE
WOS记录号WOS:000507310800007
内容类型期刊论文
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/50588]  
专题应用数学研究所
通讯作者Wang, Yongcui; Wang, Yong
作者单位1.Chinese Acad Sci, Innovat Ctr Cell Signaling Network, Inst Biochem & Cell Biol, Key Lab Syst Biol,Shanghai Inst Biol Sci, Shanghai, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, CEMS, NCMIS,MDIS, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming, Peoples R China
4.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining, Peoples R China
5.Chinese Acad Sci, Northwest Inst Plateau Biol, Qinghai Prov Key Lab Crop Mol Breeding, Xining, Peoples R China
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Wang, Yongcui,Chen, Shilong,Chen, Luonan,et al. Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs[J]. PLOS COMPUTATIONAL BIOLOGY,2019,15(12):20.
APA Wang, Yongcui,Chen, Shilong,Chen, Luonan,&Wang, Yong.(2019).Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs.PLOS COMPUTATIONAL BIOLOGY,15(12),20.
MLA Wang, Yongcui,et al."Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs".PLOS COMPUTATIONAL BIOLOGY 15.12(2019):20.
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